Defusing The Divide: Frameworks For Effective Human-AI Collaboration And Intelligence Integration

Abstract

AI-assisted decision-making has to balance between the computational robustness of the algorithm and the more ambiguous stochastic processes of the human mind, which can be based on different types of intelligence. Underlying these decisions are frameworks concerning human-centered design, causal reasoning, participative learning, and value alignment. Examples in healthcare, industry, law enforcement, and air traffic management illustrate that hybrid human-computer systems combining computational pattern recognition and human reasoning can outperform either system alone. Success is eased by models of the tool, people, and task, shared mental models, mutual trust, and strong governance.